Gesture recognition method, device, equipment and storage medium based on electromyographic signals

An electromyographic signal and gesture recognition technology, applied in character and pattern recognition, instruments, biological neural network models, etc., can solve problems such as gesture recognition errors, and achieve the elimination of misjudgment results, high recognition accuracy, and high recognition efficiency. Effect

Active Publication Date: 2022-04-01
JIHUA LAB
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[0005] The purpose of this application is to provide a gesture recognition method, device, device and storage medium based on electromyographic signals

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  • Gesture recognition method, device, equipment and storage medium based on electromyographic signals
  • Gesture recognition method, device, equipment and storage medium based on electromyographic signals
  • Gesture recognition method, device, equipment and storage medium based on electromyographic signals

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[0043] Next, the technical solutions in the present application will be described in conjunction with the present application embodiments, and it is clear that the described embodiments are merely described herein, not all of the embodiments of the present application. Components of the present application embodiments described and illustrated in the drawings herein can be arranged and designed in a variety of different configurations. Thus, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application claimed, but only the selected embodiments of the present application. Based on the embodiments of the present application, those skilled in the art will belong to the scope of this application under the premise of creative labor.

[0044] It should be noted that similar reference numerals and letters represent the similar items in the following figures, and therefore, once one ...

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Abstract

The present invention relates to the technical field of biological signal processing, and specifically discloses a gesture recognition method, device, device, and storage medium based on electromyographic signals, wherein the method includes the following steps: acquiring electromyographic signals in real time; Classification results of electromyographic signals; obtaining valid results according to the consistency of the classification results; putting the valid results into the first voting queue one by one to obtain initial gesture recognition results; putting the initial gesture recognition results into the second voting one by one In the queue, the final gesture recognition result is obtained; this method uses different classifiers to obtain consistent and effective results, removes data that is difficult to accurately classify in the EMG signal, and then performs two-stage voting through the first voting queue and the second voting queue. Vote to obtain the final gesture recognition result, which effectively removes misjudgment results caused by noise data and classifier defects, and has the characteristics of less data calculation, high recognition accuracy, and high recognition efficiency.

Description

technical field [0001] The present application relates to the technical field of biosignal processing, in particular, to a gesture recognition method, device, device and storage medium based on electromyographic signals. Background technique [0002] Electromyography (EMG) is a physiological signal that can reflect the movement mode of an animal body. The specific joint movements of the limbs are controlled by specific muscle groups. The electromyographic signals generated during the limb movement can reflect the activation mode of the muscles and the movement posture of the limbs. Therefore, the electromyographic signals are widely used in human kinematics, rehabilitation engineering and In related research of limb pathology. [0003] EMG signals can be used for gesture recognition. Existing EMG signal-based gesture recognition methods generally use a trained single classifier to classify EMG signal data to directly output gesture recognition results, which are prone to no...

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Application Information

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IPC IPC(8): G06K9/00G06N3/02
CPCG06N3/02G06F2218/08G06F2218/12
Inventor 李志建傅翼斐陈皓黄秀韦陈海龙邓涛霍震古家威何昊名高桑田王济宇张晟东牛兰蔡维嘉
Owner JIHUA LAB
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